Scale-aware Automatic Augmentation for Object Detection.pdf
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Scale-aware Automatic Augmentation for Object Detection
Yukang Chen
1
*
†
, Yanwei Li
1†
, Tao Kong
2
, Lu Qi
1
, Ruihang Chu
1∗
, Lei Li
2
, Jiaya Jia
1.3
1
The Chinese University of Hong Kong
2
ByteDance AI Lab
3
SmartMore
Abstract
We propose Scale-aware AutoAug to learn data augmen-
tation policies for object detection. We define a new scale-
aware search space, where both image- and box-level aug-
mentations are designed for maintaining scale invariance.
Upon this search space, we propose a new search met-
ric, termed Pareto Scale Balance, to facilitate search with
high efficiency. In experiments, Scale-aware AutoAug yields
significant and consistent improvement on various object
detectors (e.g., RetinaNet, Faster R-CNN, Mask R-CNN,
and FCOS), even compared with strong multi-scale training
baselines. Our searched augmentation policies are trans-
ferable to other datasets and box-level tasks beyond object
detection (e.g., instance segmentation and keypoint estima-
tion) to improve performance. The search cost is much less
than previous automated augmentation approaches for ob-
ject detection. It is notable that our searched policies have
meaningful patterns, which intuitively provide valuable in-
sight for human data augmentation design. Code and mod-
els will be available at https://github.com/Jia-Research-
Lab/SA-AutoAug.
1. Introduction
Object detection, aiming to locate as well as classify var-
ious objects, is one of the core tasks in the computer vision.
Due to the large scale variance of objects in real-world sce-
narios, it raises concerns on how to bring the scale adap-
tation to the network efficiently. Previous work handles
this challenge mainly from two aspects, namely network ar-
chitecture and data augmentation. To make the network
scale invariant, in-network feature pyramids [28, 47, 23]
and adaptive receptive fields [25] are usually employed.
Another crucial technique to enable scale invariance is data
augmentation, which is independent of specific architec-
tures, and can be generalized among multiple tasks.
This paper focuses on data augmentation for object
detection. Current data augmentation strategies can be
*
This work was done during an internship at ByteDance AI Lab. Tao
Kong is responsible for correspondence.
†
Equal contribution.
53 6759 64 74
Inference time (ms/image)
36
37
38
39
40
41
42
43
44
45
AP (%)
RetinaNet
ResNet-50
FCOS
ResNet-101
Faster R-CNN
ResNet-101
RetinaNet
ResNet-101
Mask R-CNN
ResNet-101
AutoAug-det
Dropblock
Mixup
PSIS
Stitcher
GridMask
InstaBoost
RandAug
Baseline
Scale-aware AutoAug
Figure 1: Comparison with object detection augmentation strate-
gies on MS COCO dataset. Methods in the same vertical line are
based upon the same detector. Scale-aware AutoAug outperforms
both hand-crafted and learned strategies on various detectors.
grouped into color operations (e.g., brightness, contrast, and
whitening) and geometric operations (e.g., re-scaling, flip-
ping). Among them, geometric operations, such as multi-
scale training, improve scale robustness [39, 19]. Sev-
eral hand-crafted data augmentation strategies were devel-
oped to improve performance and robustness of the detec-
tor [41, 42]. Previous work [17, 15] also improves box-level
augmentation by enriching foreground data. Though inspir-
ing performance gain has achieved, these data augmentation
strategies usually rely on heavy expert experience.
Automatic data augmentation policies were widely ex-
plored in image classification [44, 50, 37, 35, 9]. Its poten-
tial for object detection, however, was not thoroughly re-
leased. One attempt to automatically learn data augmen-
tation policies for object detectors is AutoAug-det [51]
1
,
which performs color or geometric augmentation upon the
context of boxes. It does not fully consider the scale issue
from image- and box-level, which are found, however, es-
sential in object detector design [41, 42, 17]. Moreover, the
heavy computational search cost (i.e., 400 TPU for 2 days)
impedes it from vastly practical. Thus, scale-aware property
and efficiency issue are essential to address for searching
augmentation in box-level tasks.
In this paper, we propose a new way to automatically
1
We refer it as AutoAug-det [51] to distinguish from AutoAugment [9].
arXiv:2103.17220v1 [cs.CV] 31 Mar 2021
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